def tes_non_word(self):
w = semfi.get_word("kisseli", "N", "kpv")
self.assertIsNone(w)
def test_get_word(self):
w = semfi.get_word("кань", "N", "kpv")
self.assertIsNotNone(w)
#encoding: utf-8
from uralicNLP import semfi
def print_for(word, pos, rel, target_pos):
wo = semfi.get_word(word, pos, "fin")
ws = semfi.get_by_relation(wo, rel, "fin", sort=True)
for w in ws:
if w["word2"] is not None and w["word2"]["pos"] == target_pos:
print w["word2"]["word"]
#print_for("punainen","A", "amod", "N")
wo = semfi.get_word("kettu", "N", "fin")
print semfi.get_all_relations(wo, "fin")
#print semfi.get_by_word_and_relation(semfi.get_word("karhu", "N", "fin"), semfi.get_word("olla", "V", "fin"), "nsubj", "fin")
def print_for(word, pos, rel, target_pos):
wo = semfi.get_word(word, pos, "fin")
ws = semfi.get_by_relation(wo, rel, "fin", sort=True)
for w in ws:
if w["word2"] is not None and w["word2"]["pos"] == target_pos:
print w["word2"]["word"]
def create_verb_probabilities(usr_input):
''' Uses the first input noun to find a verbs that are semantically similar. Outputs verb candidates and their probability distribution. '''
lemmas = tokenize_and_lemmatize(usr_input)
input_posses = get_pos_template(lemmas)
# print("Input POSes: " + input_posses + "\n")
# If both input words are noun. Other alternatives are not implemented.
if input_posses == 'NN':
lemma_dict = {'subject': lemmas[0], 'object': lemmas[1]}
verse = []
# Loop through both lemmas and inflect them depending on their syntactic role
for lemma in lemmas:
# print_some_input_info(lemma) # FOR DEBUGGING
for analysis in uralicApi.analyze(lemma, "fin"):
ms_desc = analysis[0].lstrip(analysis[0].split('+')[0])
# print("Analysis of the lemma: " + lemma + ms_desc + "\n") # FOR DEBUGGING
if ms_desc[1] == 'N':
if lemma == lemma_dict['subject']:
generated = uralicApi.generate(lemma + "+N+Sg+Nom",
"fin")
if lemma == lemma_dict['object']:
generated = uralicApi.generate(lemma + "+N+Sg+Gen",
"fin")
if len(generated) > 0:
verse.append(generated[0][0])
else:
print("Try with other words.")
# If the lemma is subject, choose a verb using its word relations. There's probably a better alternative for this.
if lemma == lemma_dict['subject']:
word = semfi.get_word(lemma, "N", "fin")
while True:
try:
relations = semfi.get_by_relation(word,
"dobj",
"fin",
sort=True)
break
except Exception as e:
print(
"At least one of the input words was not recognized, try with other words.\n\n"
+ e)
exit()
verbs_and_probs = []
for relation in relations:
try:
if relation['word2']['pos'] == 'V':
inflected_form = uralicApi.generate(
relation['word2']['word'] +
"+V+Act+Ind+Prs+Sg3", "fin")[0][0]
first_syllable = finmeter.hyphenate(
inflected_form).split("-")[0]
if count_syllables(
inflected_form
) == 2 and not finmeter.is_short_syllable(
first_syllable):
verbs_and_probs.append(
(relation['word2']['word'],
relation['word2']['frequency']))
except:
pass
# Sort the verb by frequency (descending order) and get rid of the top 5% frequent and the half that is least frequent
verbs_and_probs = sorted(
verbs_and_probs, key=lambda x: x[-1], reverse=True)[round((
(len(verbs_and_probs) / 100) *
5)):round(((len(verbs_and_probs) / 100) * 50))]
if len(verbs_and_probs) == 0:
print("Try with other words.")
exit()
else:
# Normalize the probabilities and choose the verb randomly
verb_candidates, probability_distribution = map(
list, zip(*verbs_and_probs))
probability_distribution = np.array(
np.array(probability_distribution) /
sum(probability_distribution))
return verb_candidates, probability_distribution, lemmas, lemma_dict, verse